Claims-Based Enumeration Sampling (CBES): Utilizing Administrative Claims Data as a Sampling Frame for Patient...

Area of Science:

• Health Services Research
• Public Health
• Health Policy

Background:

• Administrative claims data offer broad coverage but lack patient-reported outcomes.
• Patient experience surveys often suffer from selection bias due to unreliable sampling frames.
• A methodological gap exists in integrating real-world data with patient perspectives for evidence-based policymaking.

Purpose of the Study:

• To introduce and validate Claims-Based Enumeration Sampling (CBES), a novel methodology for creating representative patient samples.
• To address the limitations of existing data sources by linking administrative claims with patient survey data.
• To demonstrate the feasibility of CBES in overcoming privacy and data access challenges.

Main Methods:

• Developed Claims-Based Enumeration Sampling (CBES) using administrative claims data as a sampling frame.
• Linked individual-level survey responses with administrative claims data.
• Applied weighting methods to ensure sample representativeness, demonstrated via a case study in Tsukuba City, Japan.

Main Results:

• Successfully implemented CBES for National Health Insurance beneficiaries with diabetes.
• Overcame legal hurdles related to privacy and data access through an insurer-commissioned operation.
• Identified patient stigma and socioeconomic disparities, insights not available from claims data alone.

Conclusions:

• CBES is a robust and scalable alternative to conventional sampling methods.
• This methodology empowers policymakers to incorporate patient perspectives into healthcare policy.
• CBES advances patient-centered healthcare by capturing the experiences of underrepresented patient groups.